1. Technical Field
Oculometers are used to measure the eye gaze direction, as well as the fixation duration and dual eye binocular convergence point. Such oculometers have many potential applications in the medical, scientific, engineering, manufacturing, military, and entertainment domains. Example applications include use of an oculometer as a tool for the medical diagnosis of ocular functions, as an aid to the paraplegic handicapped, for the measurement of ocular functions and workload in human factors studies, as a measure of subject training, as a tool for fatigue monitoring, as part of an electronic safety net to detect performance degradation due to pilot incapacitation in piloted and tele-operated vehicles, as a component of an electronic intelligent pilot-vehicle interface used for adaptive aiding in piloted and tele-operated vehicles, for task scan analysis including measuring situation awareness, for human operator control of machines and interaction with computer games, and for advertisement and usability analysis. Oculometers can be designed for use with head-mounted video displays such as those that have been developed for virtual reality, stereographic displays, monocular or binocular vision helmet-mounted displays, and night vision goggles. These displays are used in piloted helicopters, vehicles, and control stations for teleoperated robotics.
2. Description of the Related Art
Prior art oculometers typically comprise a light source that illuminates the eye to be tracked, and a single light sensor that captures rays of light that are reflected from the eye. Although such oculometers provide an indication of eye position and, therefore, gaze direction, the use of a single light sensor presents various potential limitations or drawbacks. For example, a single sensor may not receive the rays reflected off of the cornea or eye interior in cases in which the user's gaze is fixed upon an object positioned at an extreme angle relative to the forward-looking direction (e.g., when the wearer is gazing laterally). As another example, if the single sensor is used to collect image data that is used to locate features in the eye of interest in three-dimensional space, multiple images may need to be captured over time, thereby increasing processing time and potentially introducing error due to eye movement over the duration in which the image data is captured.